Suppressing quantum errors by scaling a surface code logical qubit
- Creators
- Acharya, Rajeev
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Aleiner, Igor
- Allen, Richard M.
- Andersen, Trond I.
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Ansmann, Markus
- Arute, Frank
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Arya, Kunal
- Asfaw, Abraham
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Atalaya, Juan
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Babbush, Ryan
- Bacon, Dave
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Bardin, Joseph C.
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Basso, Joao
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Bengtsson, Andreas
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Boixo, Sergio
- Bortoli, Gina
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Bourassa, Alexandre
- Bovaird, Jenna
- Brill, Leon
- Broughton, Michael
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Buckley, Bob B.
- Buell, David A.
- Burger, Tim
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Burkett, Brian
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Bushnell, Nicholas
- Chen, Yu
- Chen, Zijun
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Chiaro, Benjamin
- Cogan, Josh
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Collins, Roberto
- Conner, Paul
- Courtney, William
- Crook, Alexander L.
- Curtin, Ben
- Debroy, Dripto M.
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Del Toro Barba, Alexander
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Demura, Sean
- Dunsworth, Andrew
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Eppens, Daniel
- Erickson, Catherine
- Faoro, Lara
- Farhi, Edward
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Fatemi, Reza
- Flores Burgos, Leslie
- Forati, Ebrahim
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Fowler, Austin G.
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Foxen, Brooks
- Giang, William
- Gidney, Craig
- Gilboa, Dar
- Giustina, Marissa
- Grajales Dau, Alejandro
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Gross, Jonathan A.
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Habegger, Steve
- Hamilton, Michael C.
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Harrigan, Matthew P.
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Harrington, Sean D.
- Higgott, Oscar
- Hilton, Jeremy
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Hoffmann, Markus
- Hong, Sabrina
- Huang, Trent
- Huff, Ashley
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Huggins, William J.
- Ioffe, Lev B.
- Isakov, Sergei V.
- Iveland, Justin
- Jeffrey, Evan
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Jiang, Zhang
- Jones, Cody
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Juhas, Pavol
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Kafri, Dvir
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Kechedzhi, Kostyantyn
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Kelly, Julian
- Khattar, Tanuj
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Khezri, Mostafa
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Kieferová, Mária
- Kim, Seon
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Kitaev, Alexei1
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Klimov, Paul V.
- Klots, Andrey R.
- Korotkov, Alexander N.
- Kostritsa, Fedor
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Kreikebaum, John Mark
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Landhuis, David
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Laptev, Pavel
- Lau, Kim-Ming
- Laws, Lily
- Lee, Joonho
- Lee, Kenny
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Lester, Brian J.
- Lill, Alexander
- Liu, Wayne
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Locharla, Aditya
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Lucero, Erik
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Malone, Fionn D.
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Marshall, Jeffrey
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Martin, Orion
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McClean, Jarrod R.
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McCourt, Trevor
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McEwen, Matt
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Megrant, Anthony
- Meurer Costa, Bernardo
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Mi, Xiao
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Miao, Kevin C.
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Mohseni, Masoud
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Montazeri, Shirin
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Morvan, Alexis
- Mount, Emily
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Mruczkiewicz, Wojciech
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Naaman, Ofer
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Neeley, Matthew
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Neill, Charles
- Nersisyan, Ani
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Neven, Hartmut
- Newman, Michael
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Ng, Jiun How
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Nguyen, Anthony
- Nguyen, Murray
- Niu, Murphy Yuezhen
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O'Brien, Thomas E.
- Opremcak, Alex
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Platt, John
- Petukhov, Andre
- Potter, Rebecca
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Pryadko, Leonid P.
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Quintana, Chris
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Roushan, Pedram
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Rubin, Nicholas C.
- Saei, Negar
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Sank, Daniel
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Sankaragomathi, Kannan
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Satzinger, Kevin J.
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Schurkus, Henry F.
- Schuster, Christopher
- Shearn, Michael J.
- Shorter, Aaron
- Shvarts, Vladimir
- Skruzny, Jindra
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Smelyanskiy, Vadim
- Smith, W. Clarke
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Sterling, George
- Strain, Doug
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Szalay, Marco
- Torres, Alfredo
- Vidal, Guifre
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Villalonga, Benjamin
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Vollgraff Heidweiller, Catherine
- White, Theodore
- Xing, Cheng
- Yao, Z. Jamie
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Yeh, Ping
- Yoo, Juhwan
- Young, Grayson
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Zalcman, Adam
- Zhang, Yaxing
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Zhu, Ningfeng
- Google Quantum AI
Abstract
Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10⁻⁶ logical error per cycle floor set by a single high-energy event (1.6 × 10⁻⁷ excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Additional Information
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. We are grateful to S. Brin, S. Pichai, R. Porat, J. Dean, E. Collins and J. Yagnik for their executive sponsorship of the Google Quantum AI team, and for their continued engagement and support. A portion of this work was performed in the University of California, Santa Barbara Nanofabrication Facility, an open access laboratory. J.M. acknowledges support from the National Aeronautics and Space Administration (NASA) Ames Research Center (NASA-Google SAA 403512), NASA Advanced Supercomputing Division for access to NASA high-performance computing systems, and NASA Academic Mission Services (NNA16BD14C). D.B. is a CIFAR Associate Fellow in the Quantum Information Science Program. Data availability: The data that support the findings of this study are available at https://doi.org/10.5281/zenodo.6804040. Contributions: The Google Quantum AI team conceived and designed the experiment. The theory and experimental teams at Google Quantum AI developed the data analysis, modelling and metrological tools that enabled the experiment, built the system, performed the calibrations and collected the data. The modelling was carried out jointly with collaborators outside Google Quantum AI. All authors wrote and revised the manuscript and the Supplementary Information. The authors declare no competing interests.Attached Files
Published - s41586-022-05434-1.pdf
Supplemental Material - 41586_2022_5434_MOESM1_ESM.pdf
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Additional details
- PMCID
- PMC9946823
- Eprint ID
- 122497
- Resolver ID
- CaltechAUTHORS:20230725-857426000.73
- NASA-Google
- SAA 403512
- NASA
- NNA16BD14C
- Canadian Institute for Advanced Research (CIFAR)
- Created
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2023-08-13Created from EPrint's datestamp field
- Updated
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2023-08-15Created from EPrint's last_modified field
- Caltech groups
- Institute for Quantum Information and Matter, Walter Burke Institute for Theoretical Physics